PROJECT SUMMARY: Recombination is the process by which two viruses exchange part of their genetic material to produce a ?daughter? strain that is genetically distinct. For coronaviruses (CoVs), it is an important evolutionary mechanism for generating genetic variation, but it is also an important driver of host switching. Most CoVs in humans are thought to have originated in animals (in particular, bats) and have evidence of recombination in their evolutionary history. Despite the relevance to public health, the mechanism, drivers, and limitations to recombination remain unclear. Using a combination of laboratory experiments, comparative genomics, and mathematical modeling, we will test the hypothesis that the probability of recombination between two CoVs is a function of genetic distance, and that this probability is further modified by ecological or evolutionary effects. In other words, the likelihood of recombination decreases as the genetic distance between the parental strains increases (presumably because of genetic or structural incompatibilities), but is also impacted by ecological processes such as the chance that two CoVs will actually co-occur or evolutionary selection pressure. We will test three aims: In Aim 1, we will employ laboratory studies and co-infect human and bat cell lines with pairs of CoVs over a phylogenetic gradient. This will test whether the probability of recombination is related to genetic distance between the parents, and will also show whether recombination occurs randomly throughout the genome or in particular ?hot-spots?. In Aim 2, we will evaluate naturally occurring CoVs using genomes available in public databases or from our own previous work. Presumably, naturally occurring recombinants will be the result of any genetic limitations (e.g., genetic distance), plus any ecological or evolutionary modifiers (e.g., natural selection). Thus, by comparing the laboratory (Aim 1) and observational (Aim 2) studies, we can begin to identify patterns that would remain unclear with experimental data alone, allowing us to disentangle the contributions of both genetic and ecological factors to the probability that any two viruses will recombine. This understanding can then be used to improve predictions of both the potential and probability of CoV recombination. Finally, we will extrapolate these effects to generate a spatial model that estimates of the risk of recombinant CoVs arising in different parts of the world (Aim 3). By integrating our unique global CoV surveillance data (>31,000 animals from >25 countries surveyed for CoVs) we will produce a risk map predicting regions of the world where recombination is most likely.